CVST: Fast Cross-Validation via Sequential Testing
This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
- Tammo Krueger, Mikio Braun
- Date of publication
- 2013-12-10 14:50:04
- Tammo Krueger <firstname.lastname@example.org>
- GPL (>= 2.0)
- Cochran's Q Test with Permutation
- Setup for a CVST Run.
- Construction and Handling of 'CVST.data' Objects
- Construction of Specific Learners for CVST
- Construct a Grid of Parameters
- Construct and Handle Sequential Tests.
- Perform a k-fold Cross-validation
- Fast Cross-Validation via Sequential Testing
- The Fast Cross-Validation via Sequential Testing (CVST)...
- Generate Donoho's Toy Data Sets
- Regression and Classification Toy Data Set
Files in this package